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Deciphering conductivity in PEDOT guided by machine learning: From solvent baths to charge paths

Najmeh Zahabi1,*, Ioannis Petsagkourakis2,*, Nicolas Rolland3, Ali Beikmohammadi4, Xianjie Liu1, Mats Fahlman1, Eleni Pavlopoulou5, and Igor Zozoulenko1,†

  • *These authors contributed equally to this work.
  • †Contact author: igor.zozoulenko@liu.se
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Phys. Rev. Materials 9, 105402 – Published 17 October, 2025

DOI: https://doi.org/10.1103/5h7d-yvsd

Abstract

PEDOT:Tos is a promising conducting polymer for electronic and bioelectronic applications, yet its charge transport is affected by various factors and remains challenging to optimize. This study investigates the impact of solvent posttreatment on PEDOT:Tos thin films, exploring its influence on morphology and electrical conductivity. A combined experimental-theoretical approach is employed, integrating molecular dynamics, density functional theory, and transport calculations on one hand and conductivity, GIWAXS and XPS measurements on the other hand. Moreover, we developed a machine learning (ML) framework based on convolutional neural networks with the Coulomb matrix as the predictor, and transfer integrals for multiscale transport calculations as targets. Our results reveal that solvent-induced morphological changes strongly affect charge transport, with the ML model effectively reproducing observed conductivity. The developed ML model dramatically boosts the speed of mobility calculations, enabling the analysis of large-scale polymer films that were previously beyond computational reach.

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Phys. Rev. Materials 9, 105402– Published 17 October, 2025
  • Received 16 March 2025
  • Revised 28 July 2025
  • Accepted 24 September 2025
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